Advances in Clinical Chemistry, Volume 111

Front Cover
Advances in Clinical Chemistry
Copyright
Contents
Contributors
Preface
Chapter One: Advances in antimicrobial resistance testing
1. Introduction
2. Culture-based antimicrobial susceptibility testing
2.1. Traditional AST methods
2.2. Automated AST methods
2.3. Emerging phenotypic AST methods
3. Detection of AMR using mass spectrometry
3.1. Principle of mass spectrometry
3.2. Advances in protein-based applications of mass spectrometry in AMR research
3.2.1. Detection of antimicrobial degradation or modification
3.2.2. Mass spectrometry detection based on the growth of bacteria amid an antimicrobial environment
3.2.2.1. Stable isotope (non-radioactive) labeled amino acid detection
3.2.2.2. MALDI biotyper antibiotic susceptibility test rapid assay (MBT ASTRA)
3.2.3. Detection of specific spectrum peak of biomarkers related to drug-resistance
3.3. Advances in nucleic acid-based applications of mass spectrometry in AMR research
3.4. Advances in lipid-based applications of mass spectrometry in AMR research
3.4.1. Changes in lipid composition
3.4.2. Changes in lipid abundance
3.5. Discussion
4. Detection of AMR using PCR-related technologies
4.1. Real-time PCR
4.2. Digital PCR
5. Detection of AMR using isothermal amplification technologies
5.1. Loop-mediated isothermal amplification (LAMP)
5.2. Recombinase polymerase amplification (RPA)
5.3. Rolling circle amplification (RCA)
5.4. Nucleic acid sequence-based amplification (NASBA)
5.5. Helicase-dependent amplification (HDA)
6. Detection of AMR using microarray-based techniques
6.1. DNA microarrays
6.1.1. PCR product-based arrays
6.1.2. Oligonucleotide-based DNA arrays
6.1.3. Applications of DNA microarrays in clinical diagnoses
6.1.4. Applications of DNA microarrays in AMR detection
6.1.4.1. Gram-positive bacteria
6.1.4.2. Gram-negative bacteria
6.1.4.3. Mycobacterium tuberculosis
6.1.4.4. Staphylococcus aureus
6.1.4.5. Carbapenem-resistant Gram-negative bacilli
6.2. Phenotype microarrays
7. Determining AMR using high-throughput sequencing
7.1. Culture-dependent WGS
7.2. Culture-independent mNGS
7.3. Targeted sequencing
8. Summary
Acknowledgements
References
Chapter Two: Urinary exosomes: Diagnostic impact with a bioinformatic approach
1. Significance of urinary exosomes
2. Literature search on ``urinary exosomes´´
3. Protocols for isolation of urinary exosomes
3.1. Common methods of EVs isolation
3.1.1. Ultracentrifugation
3.1.2. Precipitation
3.1.3. Filtration
3.1.4. Size exclusion chromatography (SEC)
3.1.5. Microfluidics
3.1.6. Affinity purification
4. Characterization of exosomes
4.1. Enzyme-linked immunosorbent assay (ELISA)
4.2. Nanosight nanoparticle tracking analysis (NTA)
4.3. Dynamic light scattering (DLS)
4.4. Atomic force microscopy (AFM)
4.5. Flow cytometry
5. Impact of urinary exosome cargo in disease pathogenesis
5.1. Experimental steps for the clinical application of urinary exosome-based biomarkers
5.2. miRNA in urinary exosomes
5.3. Exosome proteome studies
5.4. Correlation between exosomes in urine and structures of the urinary tract
6. Urinary exosomes in different types of cancer
6.1. Prostate cancer
6.2. Urinary bladder cancer
6.3. Renal cell carcinoma
6.4. Non-urological cancers
7. Exosome-disease correlation
8. Conclusions and future perspectives
Acknowledgments
Conflicts of interest
References
Chapter Three: Biomarkers in metabolic syndrome
1. Introduction
2. Definition and diagnosis of metabolic syndrome
3. Metabolic syndrome biomarkers
3.1. Obesity
3.1.1. Visceral adipose tissue
3.1.2. Waist circumference and waist-to-hip ratio
3.1.3. Body mass index
3.2. Insulin resistance
3.2.1. Insulin
3.2.2. C-peptide
3.2.3. Homeostatic model assessment of insulin resistance
3.2.4. Fetuin-A
3.3. Inflammatory markers (including cytokines)
3.3.1. C-reactive protein
3.3.2. Cytokines
3.3.3. Cystatin C
3.3.4. Homocysteine
3.3.5. Cell adhesion molecules (ICAM-1, VCAM-1, Selectins)
3.4. Adipocytokines
3.4.1. Leptin
3.4.2. Adiponectin
3.4.3. Leptin to adiponectin ratio
3.4.4. Adipocyte-specific fatty acid-binding protein
3.4.5. Retinol-binding protein 4
3.4.6. Fibroblast growth factor 21
3.4.7. Chemerin
3.4.8. Other adipocytokines
3.4.9. Pigment epithelium-derived factor
3.5. Oxidative stress markers
3.5.1. Uric acid
3.5.2. Oxidized low-density lipoprotein
3.5.3. Gamma-glutamyl transferase
3.5.4. Ferritin
3.5.5. Isoprostane, thiobarbituric acid-reactive substances, and malondialdehyde
3.6. Vascular biomarkers
3.6.1. Arterial stiffness
3.6.2. Breast arterial calcification
3.6.3. Cardiac age index
3.7. Lipoproteins and apolipoproteins
3.7.1. Apolipoprotein A
3.7.2. Apolipoprotein B
3.7.3. ApoB/apoA1 ratio
3.7.4. Lipoprotein(a)
3.8. Other hormones and metabolic markers
3.8.1. Aldosterone
3.8.2. Testosterone
3.8.3. Natriuretic peptide
3.8.4. Amylase
4. Conclusion
References
Chapter Four: Physins in digestive system neoplasms
1. Introduction
2. Structure of physin family proteins
2.1. Domains and structures
2.2. Phosphorylation
2.3. Glycosylation
3. Distribution of physins in digestive system
3.1. SYP
3.1.1. SYP in gut
3.1.2. SYP in pancreas
3.2. SYPL1
4. Physins as biomarkers in digestive neoplasms
4.1. SYP as a marker of neuroendocrine neoplasms in the digestive system
4.1.1. Diagnostic capacity of SYP for neuroendocrine neoplasms
4.1.2. Platelet SYP as a liquid biomarker for neuroendocrine neoplasms
4.2. SYPL1 is rising to be a biomarker for digestive tumors
4.2.1. SYPL1 as a promising biomarker for colorectal cancers
4.2.2. SYPL1 inhibits apoptosis in pancreatic ductal adenocarcinoma
4.2.3. SYPL1 predicts poor prognosis of hepatocellular carcinoma
5. Concluding remarks and prospective
Acknowledgments
References
Chapter Five: Stress system and related biomarkers in Parkinson´s disease
1. Introduction
2. Homeostasis and the stress response
3. Neurobiology of stress: Link with PD pathogenesis
3.1. Animal stress models in PD pathogenesis
3.2. Genetic/epigenetic factors
4. The clinical relationship between stress and PD
5. Stress biomarkers for Parkinson´s disease
5.1. Cortisol and other stress-related mediators
5.2. Inflammatory markers
5.3. Metabolomics/proteomics
6. Comparison of stress-related biomarkers in other disease cohorts
7. Stress and PD treatment
8. Limitations and future perspectives
9. Conclusions
References
Chapter Six: Drug testing in the era of new psychoactive substances
1. Immunoassay
1.1. Most commonly used assay formats
1.2. Sample type
1.3. Most commonly screened analytes
1.4. Expanded immunoassay drug screens
1.5. Cross-reactivity of immunoassays
1.6. New psychoactive substances (NPS)
1.6.1. Recent advances in NPS detection by immunoassay
2. Liquid chromatography tandem-mass spectrometry (LC-MS/MS)
2.1. Overview of LC-MS/MS
2.2. Liquid chromatography
2.3. Mass analysis in LC-MS/MS
2.4. Sample type and preparation for analysis
2.5. Dilution
2.6. Solid phase extraction
2.7. Hydrolysis
2.8. Drug confirmation assays
2.9. Expanded LC-MS/MS assays
2.10. Limitations of LC-MS/MS assays
2.11. Advances in LC-MS/MS testing hardware for clinical laboratories
3. High-resolution mass spectrometry
3.1. Sample type and preparation for analysis
3.2. Protein precipitation
3.3. Instrumental analysis techniques
3.4. Data acquisition strategies
3.4.1. Data dependent acquisition (DDA)
3.4.2. Data-independent acquisition (DIA)
3.5. Data processing techniques
3.5.1. Targeted analysis
3.5.2. Suspect screening
3.5.3. Non-targeted analysis
3.6. Use of LC-HRMS in NPS analysis
3.7. An example of NPS analysis using LC-HRMS
3.8. Advantages of high-resolution mass spectrometry
3.9. Limitations of high-resolution mass spectrometry
4. Conclusions
References
Index
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